Power grid control method based on neural network and current synchronization U-I droop coefficient

Abstract

The embodiment of the invention provides a power grid control method based on a neural network and a current synchronization U-I droop coefficient, and relates to the technical field of power grid control, and the method comprises the steps: designing a voltage loop and a current loop to jointly control an inverter of a power distribution area microgrid, and carrying out the control of a q-axis current through an RBF neural network, the consistency of phase angles of DG output currents of all the distributed power generation units is achieved, and the circulating current problem between the DG units in the transformer area is restrained. In addition, double-current inner-loop control of output current and network access current is adopted, and both an output current outer loop and a filter capacitor current inner loop adopt a quasi-PR current control strategy. According to the invention, effective tracking of the given current is realized, the problems of poor anti-interference performance and poor tracking performance of high-frequency signals of a traditional PI controller are avoided, the stability of the system is improved, synchronous grid-connected operation of an inverter in a power distribution area is ensured, harmonic waves are effectively suppressed, and the operation efficiency and the electric energy quality of the system are improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of power grid control, and in particular to a power grid control method based on a neural network and a current synchronization UI droop coefficient. Background Art

[0002] Large-scale renewable energy has brought great challenges to the safe and stable operation of the power system and the safe and stable operation of the distribution area. Due to the low inertia of the distributed power system composed of a high proportion of renewable energy and inverters, the traditional phasor synchronous control strategy has problems such as power-angle stability, multi-area interconnection control, and adaptation to diverse loads. In microgrids, droop control is mainly used. Since the line impedance is difficult to determine due to various factors, the traditional UI droop control effect is insufficient. At the same time, with the development of power electronics technology, the number of nonlinear elements in the power system has gradually increased, which has led to a gradual decline in power quality and a significant increase in harmonic content, among which the harm of 6n±1 (n is a natural number) subharmonics is more significant. The existence of harmonic current will not only affect the UI droop control, but also affect the distribution of active power and reactive power. Therefore, it is particularly important to suppress harmonic current.

[0003] Among the existing technical solutions, some distribute active power and reactive power according to the inverter capacity ratio. Although this solves the power angle stability problem, the system output voltage is seriously distorted under nonlinear load conditions. Some evenly distribute reactive power by adjusting the droop coefficient. However, a large droop coefficient will cause a large deviation between the inverter output voltage and the bus rated voltage, causing voltage drop and affecting system stability. Some PI controllers cannot achieve precise control of harmonics.

[0004] Therefore, it is difficult to achieve synchronous grid-connected operation and harmonic suppression of inverters in distribution stations with current technical solutions. Summary of the invention

[0005] The present disclosure proposes a power grid control method, device, equipment and storage medium based on a neural network and a current synchronization UI droop coefficient.

[0006] In order to solve the above technical problems, the technical solutions adopted by the present disclosure are as follows:

[0007] According to a first aspect of the present disclosure, a power grid control method based on a neural network and a current synchronization UI droop coefficient is provided, the method comprising:

[0008] Use the synchronous timing phasor measurement unit to synchronously measure the voltage and current data of each node of the distributed power supply units in multiple substations;

[0009] The current uses radial basis function RBF neural network for current synchronization control, and the expression of current control is:

[0010]

[0011] Among them, k p and k i are the proportional and integral parameters of current synchronous control respectively. In RBF neural network control, x=[x c ] T is the network input, x c is the input of the cth neuron, [x c ] T is the transposed matrix of the input of the cth neuron, y = [y c ] T is the output of the hidden layer of the network, y c is the output of the cth neuron in the hidden layer, [y c ] T is the transposed matrix of the output of the cth neuron in the hidden layer;

[0012] The weights of the RBF neural network are w=[w1,w2,w3,...,w m ] T , m is the number of inputs, T is the calculation of the transposed matrix, and the output of the RBF neural network is:

[0013] y t =w T x=w1x1+w2x2+w3x3+...+w m x m ,

[0014] Through the RBF neural network algorithm, the q-axis current i q To a given value i qref Approximation is performed until the error is eliminated;

[0015] When the system operation state suddenly changes, i qref -i q ≠0, the current synchronization control module participates in the regulation, and the current adopts RBF neural network for current synchronization control, and continuously adjusts θ based on the expression of current control. ref The size of q =0, the system reaches a stable state after adjustment;

[0016] An inductor-capacitor-inductor LCL filter is used to filter the distributed power supply units in the stable state at high frequency, and then connected to the power grid or load. The control strategy for harmonic compensation adopts a current double closed-loop structure of the output current or grid current outer loop and the filter capacitor current inner loop; both the output current outer loop and the filter capacitor current inner loop adopt a quasi-proportional-resonant PR current control strategy;

[0017] The grid-connected current outer loop is composed of a proportional control branch, a fundamental wave control branch, and a harmonic control branch in parallel, wherein the harmonic suppression branch is used to suppress each harmonic component. When the current error is greater than a threshold, the 5th, 7th, 11th, and 13th harmonics with greater harmonics are preferentially suppressed.

[0018] In some implementations of the first aspect, the method further includes:

[0019] When I i >(I i +I j ) / 2, indicating that the equivalent impedance Z i <Z j , the current deviation is positive, and a droop parameter adjustment value greater than 0 is obtained through the PI regulator, which increases the droop parameter of the converter and improves the equivalent impedance Z of the converter. i , and finally achieve Z i =Z j , to achieve the current sharing effect; when I i <(I i +I j ) / 2, indicating that the equivalent impedance Z i >Z j , the current deviation is negative, and a droop parameter adjustment value less than 0 is obtained through the PI regulator, which reduces the droop parameter of the converter and reduces the equivalent impedance Z of the converter. i , and finally achieve Z i =Z j ; The droop coefficient after adaptive adjustment is γ i * =γ i +σ i As long as there is a current deviation ΔI, the droop factor will be adjusted continuously until ΔI is zero, where I i is the current value associated with node i, I j is the current value associated with node j, γ i is the original droop coefficient of node i, σ i is the corresponding correction coefficient of node i.

[0020] In some implementations of the first aspect, the grid current outer loop corresponds to two working modes. When the input current I h is the load current I L When the input current I h When it is 0, the harmonics of the system are controlled and suppressed.

[0021] In some implementations of the first aspect, both the output current outer loop and the filter capacitor current inner loop adopt a quasi-PR current control strategy, and the transfer function of the quasi-PR controller is expressed as:

[0022]

[0023] Among them, K p is the proportional gain, k nr is the resonant gain, ω nc is the cutoff frequency, ω n is the natural angular frequency, and s is a complex variable.

[0024] In some implementations of the first aspect, the current error signal of the current outer loop is summed by proportional control and proportional R control of each harmonic, and each separated harmonic is subjected to separate R control, and the harmonic controller only performs gain steady-state control on integer fundamental frequencies;

[0025] Among them, the expressions of the fundamental wave control branch and the harmonic wave control branch are:

[0026]

[0027] Among them, K p is the proportional gain, k nr is the resonant gain, ω nc is the cutoff frequency, ω n is the natural angular frequency, and s is a complex variable.

[0028] In some implementations of the first aspect, the method further includes:

[0029] The neural network algorithm is used to extract the harmonic components of the system, where the harmonics in the system are expressed as:

[0030]

[0031] Where t represents the time variable, b is the fundamental component, w is the angular frequency, w = 2πf, f is the frequency of the signal, ε is the harmonic order, A ε , ε are the amplitude and phase of the ε-th harmonic respectively.

[0032] In some implementations of the first aspect, in the neural network model, the input matrix is ​​represented as:

[0033] C=[c1(t),c2(t),c3(t),L,c 2n (t)]=[sin(wt),cos(wt),sin(2wt),cos(2wt),L,sin(nwt),cos(nwt)],

[0034] The weight matrix is ​​expressed as:

[0035] W=[w1(t),w2(t),w3(t),L,w 2n (t)]=[A1 sinψ1,A1cosψ1,A2 sinψ2,A2 cosψ2,L,A n sinψ n ,A n cosψ n ],

[0036] The output vector of the neural network is the product of the input matrix and the weight matrix Getting the error from the neural network model y(t) is the theoretical calibration value. After the error e(t) is processed by the least mean square LMS algorithm, the weight matrix W is readjusted according to the principle of minimizing the error target G. The error target function is expressed as:

[0037]

[0038] Each harmonic is extracted through the neural network model, and the amplitude and phase of each harmonic are expressed as:

[0039]

[0040] According to a second aspect of the present disclosure, a power grid control device based on a neural network and a current synchronization UI droop coefficient is provided, the device comprising:

[0041] The synchronous timing phasor measurement unit is used to synchronously measure the voltage and current data of each node of the distributed power supply units in multiple substations;

[0042] The current uses radial basis function RBF neural network for current synchronization control, and the expression of current control is:

[0043]

[0044] Among them, k p and k i are the proportional and integral parameters of current synchronous control respectively. In RBF neural network control, x=[x c ] T is the network input, x c is the input of the cth neuron, [x c ] T is the transposed matrix of the input of the cth neuron, y = [y c ] T is the output of the hidden layer of the network, y c is the output of the cth neuron in the hidden layer, [y c ]T is the transposed matrix of the output of the cth neuron in the hidden layer;

[0045] The weights of the RBF neural network are w=[w1,w2,w3,...,w m ] T , m is the number of inputs, T is the calculation of the transposed matrix, and the output of the RBF neural network is:

[0046] y t =w T x=w1x1+w2x2+w3x3+...+w m x m ,

[0047] Through the RBF neural network algorithm, the q-axis current i q To a given value i qref Approximation is performed until the error is eliminated;

[0048] The current synchronization control module is used when the system operating state suddenly changes and i qref -i q ≠0, the current is controlled by RBF neural network, and θ is adjusted continuously based on the current control expression. ref The size of q =0, the system reaches a stable state after adjustment;

[0049] The inductor-capacitor-inductor LCL filter is used to filter the distributed power supply units in the stable state at high frequencies, and then connect them to the grid or load. The control strategy for harmonic compensation adopts a current double closed-loop structure of the output current or grid current outer loop and the filter capacitor current inner loop; both the output current outer loop and the filter capacitor current inner loop adopt a quasi-proportional-resonant PR current control strategy;

[0050] Among them, the grid-connected current outer loop is composed of a proportional control branch, a fundamental wave control branch, and a harmonic control branch in parallel, wherein the harmonic suppression branch is used to suppress each harmonic component. When the current error is greater than a threshold, the 5th, 7th, 11th, and 13th harmonics with greater harmonics are preferentially suppressed.

[0051] According to a third aspect of the present disclosure, an electronic device is provided, the electronic device comprising:

[0052] at least one processor;

[0053] and a memory communicatively coupled to the at least one processor;

[0054] It is characterized in that the memory stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor to implement the method of the first aspect of the present disclosure.

[0055] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the method according to the first aspect of the present disclosure is implemented.

[0056] The beneficial effects of a power grid control method, device, equipment and storage medium based on neural network and current synchronization UI droop coefficient provided by the present disclosure are as follows: by designing the voltage loop and the current loop to jointly control the inverter of the distribution station microgrid, and controlling the q-axis current through the RBF neural network, the consistency of the output current phase angle of each distributed generation unit DG is achieved, and the circulation problem between the DG units in the station area is suppressed. In addition, a dual current inner loop control of the output current and the grid current is adopted, and the output current outer loop and the filter capacitor current inner loop both adopt a quasi-PR current control strategy. Effective tracking of a given current is achieved, avoiding the problems of poor anti-interference performance and tracking performance of high-frequency signals of traditional PI controllers, improving the stability of the system, ensuring the synchronous grid-connected operation of the inverter in the distribution station area, and effectively suppressing harmonics, improving the operating efficiency and power quality of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the drawings required for use in the embodiment of the present disclosure will be briefly introduced below. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0058] Figure 1 A flow chart of a power grid control method based on a neural network and a current synchronization UI droop coefficient provided in an embodiment of the present disclosure is shown;

[0059] Figure 2 A microgrid structure diagram of a distribution station area provided by an embodiment of the present disclosure is shown;

[0060] Figure 3 A control block diagram provided by an embodiment of the present disclosure is shown;

[0061] Figure 4 A control block diagram of an inverter harmonic compensation system provided by an embodiment of the present disclosure is shown;

[0062] Figure 5 A block diagram of current synchronization control based on RBF neural network provided by an embodiment of the present disclosure is shown;

[0063] Figure 6 A control block diagram of an adaptive droop coefficient provided by an embodiment of the present disclosure is shown;

[0064] Figure 7 A model diagram of harmonic extraction based on a neural network provided by an embodiment of the present disclosure is shown;

[0065] Figure 8 A block diagram of a power grid control device based on a neural network and a current synchronization UI droop coefficient provided by an embodiment of the present disclosure is shown;

[0066] Fig. 9 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0067] The features and exemplary embodiments of various aspects of the present disclosure will be described in detail below. In order to make the purposes, technical solutions and advantages of the present disclosure clearer, the present disclosure will be further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only configured to explain the present disclosure and are not configured to limit the present disclosure. For those skilled in the art, the present disclosure can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present disclosure by showing examples of the present disclosure.

[0068] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0069] In the present disclosure, in order to solve the above technical problems, the inverter of the distribution substation microgrid is jointly controlled by designing a voltage loop and a current loop, and the q-axis current is controlled by a radial basis function (RBF) neural network, so as to achieve the consistency of the output current phase angle of each distributed generation unit (DG), and suppress the circulation problem between the DG units in the substation. In addition, a dual current inner loop control of the output current and the grid current is adopted, and the output current outer loop and the filter capacitor current inner loop both adopt a quasi-proportional-resonant (PR) current control strategy. Effective tracking of a given current is achieved, and the problems of poor anti-interference performance and tracking performance of high-frequency signals of the traditional proportional-integral (PI) controller are avoided, the stability of the system is improved, the synchronous grid-connected operation of the distribution substation inverter is ensured, and harmonics are effectively suppressed, thereby improving the operating efficiency and power quality of the system. The technical solution provided by the embodiment of the present disclosure is described below in conjunction with the accompanying drawings.

[0070] Figure 1 A flow chart of a power grid control method based on a neural network and a current synchronization UI droop coefficient provided according to an embodiment of the present disclosure is shown.

[0071] like Figure 1 As shown, the power grid control method based on neural network and current synchronization UI droop coefficient may include:

[0072] S101, using a synchronous timing phasor measurement unit to synchronously measure voltage and current data of each node of distributed power supply units in multiple substations;

[0073] After the phasor measurement unit obtains the precise time signal from the central clock source, the sampling frequency is determined, and the sampling frequency can be directly obtained from the database (different sampling frequencies can be set for different substations, such as small photovoltaic substations: sampling frequency 1000Hz; large wind power substations: sampling frequency 2000Hz to 5000Hz; complex substations (including energy storage and photovoltaics): sampling frequency 2000Hz to 5000Hz; fault detection key substations: sampling frequency 5000Hz or higher).

[0074] Based on the determined sampling frequency, multiple substations are synchronously measured to obtain the voltage and current data of each node of the distributed power supply unit. In order to ensure that the phasor measurement unit can accurately obtain the voltage and current data, it is necessary to ensure that the state of the phasor measurement unit is stable. The stability of the phasor measurement unit can be evaluated through various physical data. Specifically, the response time T of each phasor measurement unit at each node of the distributed power supply unit can be obtained. x, synchronization accuracy B x and phasor measurement error TVE (one or more phasor measurement units can be deployed on the power unit node. If multiple phasor measurement units are deployed, the voltage and current data of each node finally uploaded to the data center are the average values ​​measured by multiple phasor measurement units), where T x It is the time (in ms) from when the phasor measurement unit receives the instruction to collect voltage and current data to when it starts collecting data. x is the absolute value of the difference between the actual time of the phasor measurement unit and the standard time of the central clock source (in μs). The calculation of TVE is based on the actual measured voltage or current amplitude (in this embodiment, the actual measured voltage amplitude V is selected). m , in V) and the measured phase angle θ m (unit is angle) is calculated, the specific formula is:

[0075]

[0076] Among them, V t is the true amplitude of the current or voltage. In this embodiment, the true amplitude of the voltage is selected (ideally, the precise value of the voltage, which is measured by high-precision equipment under laboratory conditions and used as a comparison benchmark), θ t is the true phase angle (ideally, the exact value of the phase angle, which is measured by high-precision equipment under laboratory conditions and used as a comparison benchmark).

[0077] TVE combines the amplitude error and the phase angle error to comprehensively evaluate the overall measurement error of the phasor measurement unit through a square root of the sum of squares formula. The smaller the TVE, the closer the PMU's measurement is to the true value, and the higher the stability and accuracy of the measurement. TVE is usually expressed as a percentage. For example, if the TVE is 1%, it means that the combined error between the measurement result and the true value is 1%. Under steady-state conditions, TVE is usually required to be less than 1%; under dynamic conditions, the allowable TVE is usually no more than 3% to ensure that the PMU in the power grid can accurately monitor changes in the power system.

[0078] T x , B x And the analysis basis of TVE as the state of the phasor measurement unit, specifically, based on the temperature drift ΔV of the environment in which the phasor measurement unit is located temp (ΔV temp=S*ΔT, S is the temperature sensitivity coefficient, which is expressed in terms of voltage change per degree Celsius in this embodiment, with the unit of V / °C, and ΔT is the change in ambient temperature, usually expressed in degrees Celsius) to determine the TVE allowable deviation ΔTVE (the temperature drift is compared with each temperature drift interval stored in the database to determine the temperature drift interval corresponding to the calculated temperature drift, thereby obtaining the ΔTVE corresponding to the temperature drift interval from the database), and the stability evaluation value can be calculated by the following formula:

[0079]

[0080] Where a is T stored in the database x The weight factor of B is stored in the database. x The weight factor of TVE deviation stored in the database is c, c To request the response time, B c To require synchronization accuracy, C TVE is the required phasor measurement error, and δ is the stability evaluation value.

[0081] When the stability assessment value is greater than the required threshold, it means that the current state of the phasor measurement unit is abnormal and an early warning should be issued. If only one phasor measurement unit is deployed on the power supply unit node, then while issuing an early warning to the phasor measurement unit, an early warning should also be issued that the voltage and current data of the node cannot be measured by the phasor measurement unit. If multiple phasor measurement units are deployed, voltage and current data can be collected through other phasor measurement units whose stability assessment values ​​are not greater than the required threshold. When and only when all phasor measurement units are abnormal, an early warning is issued that the voltage and current data of the node cannot be measured by the phasor measurement unit.

[0082] For a node where multiple phasor measurement units are deployed, if all the phasor measurement units of the node are normal within an evaluation cycle, the actually measured voltage amplitude in the above TVE calculation formula is replaced by the voltage average of multiple phasor measurement units, and the response time and synchronization accuracy in the stability evaluation value are replaced by the average response time and average synchronization accuracy. The calculated stability evaluation value is recorded as the averaged stability evaluation value.

[0083] When and only when the averaged stability evaluation value is greater than the required threshold, an early warning of an abnormal phasor measurement unit is issued, and the faulty phasor measurement unit is checked one by one according to the stability evaluation value, and other normal phasor measurement units are used to collect voltage and current data.

[0084] In the next evaluation cycle, all phasor measurement units of the node are evaluated one by one to see if they are normal. If they are normal, the averaged stability evaluation value is used to perform the evaluation in the next cycle.

[0085] S102, current synchronous control using radial basis function (RBF) neural network;

[0086] Among them, the expression of current control is:

[0087]

[0088] Among them, k p and k i are the proportional and integral parameters of current synchronous control respectively. In RBF neural network control, x=[x c ] T is the network input, x c is the input of the cth neuron, [x c ] T is the transposed matrix of the input of the cth neuron, y = [y c ] T is the output of the hidden layer of the network, y c is the output of the cth neuron in the hidden layer, [y c ] T is the transposed matrix of the output of the cth neuron in the hidden layer;

[0089] The weights of the RBF neural network are w=[w1,w2,w3,...,w m ] T , m is the number of inputs, T is the calculation of the transposed matrix, and the output of the RBF neural network is:

[0090] y t =w T x=w1x1+w2x2+w3x3+...+w m x m ,

[0091] Through the RBF neural network algorithm, the q-axis current i q To a given value i qref Approximation is performed until the error is eliminated;

[0092] S103, when the system operation state suddenly changes, i qref -i q ≠0, the current synchronization control module participates in the regulation, and the current uses the RBF neural network for current synchronization control, and continuously adjusts θ based on the current control expression. ref The size of q=0, the system reaches a stable state after adjustment;

[0093] S104, using an inductor-capacitor-inductor LCL filter to perform high-frequency filtering on the distributed power supply unit in the station area in a stable state, and then connecting it to the power grid or load. The control strategy for harmonic compensation adopts a current double closed-loop structure of an output current or grid current outer loop and a filter capacitor current inner loop; both the output current outer loop and the filter capacitor current inner loop adopt a quasi-proportional-resonant PR current control strategy;

[0094] Among them, the grid-connected current outer loop is composed of a proportional control branch, a fundamental wave control branch, and a harmonic control branch in parallel, wherein the harmonic suppression branch is used to suppress each harmonic component. When the current error is greater than a threshold, the 5th, 7th, 11th, and 13th harmonics with greater harmonics are preferentially suppressed.

[0095] The grid control method based on neural network and current synchronization UI droop coefficient provided by the present disclosure controls the inverter of the distribution substation microgrid by designing voltage loop and current loop together, controls the q-axis current by RBF neural network, realizes the consistency of the output current phase angle of each distributed generation unit DG, and suppresses the circulation problem between the DG units in the substation. In addition, the dual current inner loop control of output current and grid current is adopted, and the output current outer loop and the filter capacitor current inner loop both adopt quasi-PR current control strategy, realizes the effective tracking of the given current, avoids the poor anti-interference performance and tracking performance of high-frequency signals of the traditional PI controller, improves the stability of the system, ensures the synchronous grid-connected operation of the distribution substation inverter, and effectively suppresses harmonics, improves the operation efficiency and power quality of the system.

[0096] When I i >(I i +I j ) / 2, indicating that the equivalent impedance Z i <Z j , the current deviation is positive, and a droop parameter adjustment value greater than 0 is obtained through the PI regulator, which increases the droop parameter of the converter and improves the equivalent impedance Z of the converter. i , and finally achieve Z i =Z j , to achieve the current sharing effect; when I i <(I i +I j ) / 2, indicating that the equivalent impedance Z i >Z j , the current deviation is negative, and a droop parameter adjustment value less than 0 is obtained through the PI regulator, which reduces the droop parameter of the converter and reduces the equivalent impedance Z of the converter. i , and finally achieve Z i =Z j; The droop coefficient after adaptive adjustment is γ i * =γ i +σ i As long as there is a current deviation ΔI, the droop factor will be adjusted continuously until ΔI is zero, where I i is the current value associated with node i, I j is the current value associated with node j, γ i is the original droop coefficient of node i, σ i is the corresponding correction coefficient of node i.

[0097] In some embodiments, the above-mentioned grid-connected current outer loop corresponds to two working modes. When the input current I h is the load current I L When the input current I h When it is 0, the harmonics of the system are controlled and suppressed.

[0098] In some embodiments, both the output current outer loop and the filter capacitor current inner loop adopt a quasi-PR current control strategy, and the transfer function of the quasi-PR controller is expressed as:

[0099]

[0100] Among them, K p is the proportional gain, k nr is the resonant gain, ω nc is the cutoff frequency, ω n is the natural angular frequency, and s is a complex variable.

[0101] In some embodiments, the current error signal of the current outer loop is summed through proportional control and proportional R control of each harmonic, and each separated harmonic is subjected to separate R control, and the harmonic controller only performs gain steady-state control on the integer fundamental frequency;

[0102] Among them, the expressions of the fundamental wave control branch and the harmonic wave control branch are:

[0103]

[0104] Among them, K p is the proportional gain, k nr is the resonant gain, ω nc is the cutoff frequency, ω n is the natural angular frequency, and s is a complex variable.

[0105] In some embodiments, Figure 1 The method may also include:

[0106] The neural network algorithm is used to extract the harmonic components of the system, where the harmonics in the system are expressed as:

[0107]

[0108] Where t represents the time variable, b is the fundamental component, w is the angular frequency, w = 2πf, f is the frequency of the signal, ε is the harmonic order, A ε , ε are the amplitude and phase of the ε-th harmonic respectively.

[0109] In some embodiments, in a neural network model, the input matrix may be represented as:

[0110] C=[c1(t),c2(t),c3(t),L,c 2n (t)]=[sin(wt),cos(wt),sin(2wt),cos(2wt),L,sin(nwt),cos(nwt)],

[0111] The weight matrix is ​​expressed as:

[0112] W=[w1(t),w2(t),w3(t),L,w 2n (t)]=[A1 sinψ1,A1cosψ1,A2 sinψ2,A2 cosψ2,L,A n sinψ n ,A n cosψ n ],

[0113] The output vector of the neural network is the product of the input matrix and the weight matrix Getting the Error from the Neural Network Model y(t) is the theoretical calibration value. After the error e(t) is processed by the least mean square LMS algorithm, the weight matrix W is readjusted according to the principle of minimizing the error target G. The error target function is expressed as:

[0114]

[0115] Each harmonic is extracted through the neural network model, and the amplitude and phase of each harmonic are expressed as:

[0116]

[0117] Figure 2 A diagram showing the structure of a microgrid in a distribution station area provided according to an embodiment of the present disclosure is shown.

[0118] like Figure 2As shown in the figure, the distribution station area microgrid is composed of various distributed power sources (DG) and their corresponding loads. Each distributed power source (DG) is connected to the DC bus through a power electronic inverter and an LCL filter, and is connected to the common bus (Point of Common Coupling, PCC) under the control of a fixed-frequency synchronization signal to supply power to local loads or be incorporated into the AC power grid through an area distribution transformer.

[0119] Since the lines connected to each module are inconsistent, the difference in line parameters will cause the phase angle of the output current between the DG units in each substation to be different. The deviation of the phase angle will cause circulating current between each substation, reducing the operation efficiency of the entire microgrid. To solve the above problems, this paper proposes a current synchronization UI droop control strategy based on RBF neural network and current synchronization UI amplitude adaptive droop coefficient control. The control block diagram is shown in the figure. Figure 3 As shown in the figure, a single distributed power source DG1 is analyzed. d 、i q is the dq axis current component of the output current of the DG1 unit in the station area; i 01 is the output current of DG1 unit; u 01 , U 01 are the no-load output voltage and amplitude of DG1 respectively; u PCC is the connection point voltage, γ i * is the adaptive droop control coefficient, AC represents the AC power grid, θ ref , U ref are the phase angle and amplitude reference values ​​of the output voltage, u dref 、u qref Voltage reference value of the voltage loop dq axis.

[0120] The control strategy mainly consists of three parts: voltage and current double closed-loop control part, current synchronization control module and UI amplitude droop control module. Among them, in the current synchronization control module, the RBF neural network is used to approximate the q-axis current to eliminate the error. Based on this control module, the phase angle of the output current of each DG unit is made consistent, thereby suppressing the circulating current between the DGs in each substation, effectively improving the power distribution between the DGs, improving the control performance of the system, and improving the operating efficiency. In the UI amplitude droop control module, since the line impedance in actual applications is difficult to determine due to various factors, there are great disadvantages in configuring the droop coefficient only by relying on the own capacity of each DG in the substation. Therefore, this paper makes an adaptive adjustment to the droop coefficient to achieve power balancing and reasonable distribution of active and reactive power with the adaptive droop coefficient.

[0121] The harmonic compensation control strategy of the DG inverter in the distribution area is explained by taking the DG1 unit in the distribution area as an example. Figure 4The control block diagram of the inverter harmonic compensation system designed in this paper is given. The figure shows the current double inner loop control structure diagram of harmonic suppression, in which the output value of the voltage loop is given by Figure 3 The voltage loop in the circuit is provided.

[0122] The inverter first uses an LCL filter for high-frequency filtering, and then connects to the grid or load. The control strategy for harmonic compensation uses the output current (or grid current i1) outer loop and the filter capacitor current (i C ) inner loop of the current double closed-loop structure. Both the output current outer loop and the filter capacitor current inner loop adopt a quasi-PR current control strategy. It can effectively track the given current, avoid the poor anti-interference performance and tracking performance of high-frequency signals of the traditional PI controller, and improve the stability of the system. The filter capacitor current inner loop can suppress the peak resonance in the LCL and improve the reliability and stability of the DG unit. The composition of the grid current outer loop is different from the traditional harmonic detection circuit. It is composed of a proportional control branch, a fundamental control branch, and a harmonic control branch in parallel. The harmonic suppression branch can suppress each harmonic component. When the current error is large, it can choose to give priority to suppressing the more harmful 5th, 7th, 11th, and 13th harmonics, corresponding to the switch quantities K5, K7, and K 11 , K 13 At the same time, this paper designs a method for extracting harmonic components based on neural network, which has a simple structure and fast dynamic response. In addition, two working modes are designed for the outer loop of the grid current, corresponding to the input current I h When the input current I h is the load current I L When the input current I h When it is 0, the main purpose is to control and suppress the harmonics of the system to improve the current quality of the inverter.

[0123] In order to solve the problems of synchronous grid-connected operation and harmonic suppression of inverters in distribution stations, this paper adopts a voltage and current dual closed-loop control strategy for microgrid inverters in distribution stations based on synchronous timing and current synchronization UI amplitude droop control scheme. The current synchronization control uses RBF neural network to approximate the q-axis current to eliminate errors. Based on this control module, the phase angle of the output current of each DG unit is made consistent, thereby suppressing the circulating current between DGs in each substation, effectively improving the power distribution between DGs, improving the control performance of the system, and improving the operating efficiency. In the UI amplitude droop control, since the line impedance in actual applications is difficult to determine due to various factors, there are great disadvantages in configuring the droop coefficient based solely on the capacity of each DG in the substation. Therefore, this paper adaptively adjusts the droop coefficient to achieve power balancing and reasonable distribution of active and reactive power with adaptive droop coefficients. The output current synchronization of each DG unit is successfully achieved, and the stable operation of the output voltage in grid-connected and island modes is ensured.

[0124] In order to solve the problems of current tracking, harmonic detection and suppression, a harmonic compensation and suppression strategy without harmonic detection is proposed. This strategy adopts dual current inner loop control of output current and grid current, which can effectively compensate and suppress harmonics. Both the output current outer loop and the filter capacitor current inner loop adopt quasi-PR current control strategy. It can effectively track the given current, avoid the poor anti-interference performance and tracking performance of high-frequency signals of the traditional PI controller, and improve the stability of the system. The grid current outer loop is composed of proportional control, fundamental control, and harmonic control in parallel. The harmonic control branch is extracted by the harmonic component extraction module, and the harmonic components can be suppressed by controlling the switches of each harmonic branch. At the same time, this paper designs a method for extracting harmonic components based on neural network, which has a simple structure and fast dynamic response. In addition, two working modes are designed for the grid current outer loop. The system can flexibly select the working mode according to the capacity of the load carried by the inverter in the substation, effectively suppressing system harmonics, improving waveform quality, and improving inverter efficiency.

[0125] Under the control strategy proposed in this paper, the grid-connected inverter in the distribution station area can obtain stable and reliable output voltage regardless of grid-connected operation or island operation. The system can flexibly select the working mode according to the load capacity. The voltage total harmonic distortion rate is kept in a small range, and the THD value is below 3%, which effectively improves the waveform quality of the inverter.

[0126] In a specific example, since the lines connected to each module are inconsistent, the difference in line parameters will cause the phase angle of the output current between the DG units in each substation to be different. The deviation of the phase angle will cause the circulation between each substation, reducing the operation efficiency of the entire microgrid. To solve the above problems, this paper proposes a current synchronization UI droop control strategy based on RBF neural network and current synchronization UI amplitude adaptive droop coefficient control. The control block diagram is shown in the figure. Figure 3 As shown in the figure, a single distributed power source DG1 is analyzed. The control strategy mainly consists of three parts: voltage and current double closed-loop control part, current synchronization control module and UI amplitude droop control module. d 、i q is the dq axis current component of the output current of the DG1 unit in the station area; i 01 is the output current of DG1 unit; u 01 , U 01 are the no-load output voltage and amplitude of DG1 respectively; u PCC is the connection point voltage, γ i * is the adaptive droop control coefficient, AC represents the AC power grid, θ ref , U ref are the phase angle and amplitude reference values ​​of the output voltage, u dref 、u qref Voltage reference value of the voltage loop dq axis.

[0127] This design uses PMU (phasor measurement unit, PMU) control with satellite synchronous timing to achieve synchronous measurement of voltage and current data of each node of the DG unit in the substation area to obtain the voltage and current status information of the entire substation area. The central controller obtains the line status of the entire substation area in real time by reading the PMU data of each node, without the need for complex power flow calculations, improving the control reliability of the substation distribution network, facilitating real-time adjustment of the operating status of the DG unit, simplifying the complexity of system scheduling and control, and improving response speed and stability.

[0128] Since the phase angles of the output currents of the various DG units are consistent, that is, the phase angles of the output currents of the various DG units relative to the d-axis are 0, the q-axis current reference signal i qref =0 is set as the control target, and the current is synchronously controlled using the RBF neural network. The expression of current control is:

[0129]

[0130] In the formula, k p and k i are the proportional and integral parameters of current synchronous control respectively. In RBF neural network control, x=[x c] T is the network input, x c is the input of the cth neuron, [x c ] T is the transposed matrix of the input of the cth neuron, y = [y c ] T is the output of the hidden layer of the network, y c is the output of the cth neuron in the hidden layer, [y c ] T is the transposed matrix of the output of the cth neuron in the hidden layer. The weights of the RBF neural network are defined as w = [w1, w2, w3, ..., w m ] T , so the output of the RBF neural network is:

[0131] y t =w T x=w1x1+w2x2+w3x3+...+w m x m ,

[0132] The q-axis current is approximated by the RBF neural network. The RBF neural network control can improve the dynamic response of the system and the fault tolerance of the control system. The current synchronization control block diagram based on the RBF neural network is shown in Figure 5 As shown. Through the RBF neural network algorithm in the figure, the q-axis current i q To a given value i qref Approximation is performed until all errors are eliminated and control performance is improved.

[0133] Since the DG units in each area are synchronized by Beidou satellite / GPS, the rising edge of the Beidou satellite / GPS timing pulse is required to be consistent with the d-axis of the two-phase rotating dq coordinate system. In order to synchronize the output current of each DG unit, the i of the DG units in the area is required to be q = 0. When the system operation state undergoes an instantaneous mutation, i qref -i q ≠0, the current synchronization control module is involved in the regulation, and the current uses the RBF neural network for current synchronization control, and continuously adjusts θ through the current control expression ref The size of q =0.

[0134] When the system reaches a stable state after adjustment, i q When the control is adjusted to a given value of 0, the phase angle of the output current of each DG unit in the substation is 0, and the loss generated in the line is ignored. i The active output power P of the unit i and reactive power Qi Can be obtained respectively:

[0135]

[0136] Where U PCC is the voltage amplitude corresponding to the common connection point, θ is the corresponding phase angle, I 0i It is a distributed power source DG in the distribution area i The output current amplitude of the unit.

[0137] From the voltage vector relationship, it can be seen that when the system is in a steady state, the phase angle of the output current of each DG unit in the substation is 0, and the output power factor of the DG unit is consistent with the load power factor. At this time, the circulating current between the systems is almost non-existent, which can effectively improve the power distribution between DG units. In addition, since the current synchronization control module adopts the RBF neural network control algorithm, it effectively improves the dynamic response of the system and the fault tolerance of the control system, and also reduces the influence of the reference signal fluctuation on the output voltage distortion.

[0138] The UI amplitude droop control module is based on the DG no-load output voltage amplitude U 01 The voltage amplitude reference signal is solved by the d-axis current amplitude to participate in the system regulation. The output of each DG unit in the substation is reasonably distributed according to the droop coefficient to ensure the stable operation of the output voltage. q = 0, i d It is the power frequency component amplitude of the output current of the DG unit in the distribution station area, so the UI amplitude droop control can be designed as:

[0139] U ref =U 01 -γi d ,

[0140] Generally speaking, the output of each DG unit in the distribution area is distributed in inverse proportion to its capacity. According to the UI amplitude droop control formula, if the UI of each DG unit in the distribution area is 01 are exactly the same. When the influence of line impedance is ignored, the value of the droop coefficient should be inversely proportional to the current flowing through the lines in each substation. However, in practical applications, line impedance is difficult to determine due to various factors. Therefore, this paper makes an adaptive adjustment to the droop coefficient. Figure 6 The control block diagram of the adaptive droop coefficient is given. i >(I i +I j ) / 2, indicating that the equivalent impedance Z i <Z j , the current deviation is positive, and a droop parameter adjustment value greater than 0 is obtained through the PI regulator, which increases the droop parameter of the converter and improves the equivalent impedance Z of the converter. i, and finally achieve Z i =Z j , to achieve the current sharing effect. i <(I i +I j ) / 2, indicating that the equivalent impedance Z i >Z j , the current deviation is negative, and a droop parameter adjustment value less than 0 is obtained through the PI regulator, which reduces the droop parameter of converter i and reduces the equivalent impedance Z of the converter. i , and finally achieve Z i =Z j The droop coefficient after adaptive adjustment is γ i * =γ i +σ i As long as there is a current deviation ΔI, the droop factor will be adjusted continuously until ΔI is zero, where I i is the current value associated with node i, I j is the current value associated with node j, γ i is the original droop coefficient of node i, σ i is the corresponding correction coefficient of node i. The introduction of the adaptive droop coefficient can reasonably divide the power of two different converter systems and achieve synchronous regulation.

[0141] Take the DG1 unit in the distribution area as an example. Figure 4 The control block diagram of the inverter harmonic compensation is given. First, the LCL filter is used for high-frequency filtering, and then connected to the grid or load. The control strategy of harmonic compensation uses the output current (or grid current i1) outer loop and the filter capacitor current (i C ) inner loop of the current double closed-loop structure. Both the output current outer loop and the filter capacitor current inner loop adopt a quasi-PR current control strategy. It can effectively track the given current, avoid the poor anti-interference performance and tracking performance of high-frequency signals of the traditional PI controller, and improve the stability of the system. The filter capacitor current inner loop can suppress the peak resonance in the LCL and improve the reliability and stability of the DG unit. The composition of the grid current outer loop is different from the traditional harmonic detection circuit. It is composed of a proportional control branch, a fundamental control branch, and a harmonic control branch in parallel. The harmonic suppression branch can suppress each harmonic component. When the current error is large, it can choose to give priority to suppressing the more harmful 5th, 7th, 11th, and 13th harmonics, corresponding to the switch quantities K5, K7, and K 11 , K 13 In addition, the grid current outer loop is designed with two working modes, corresponding to the input current I h When the input current I h is the load current I LWhen the input current I h When it is 0, the main purpose is to control and suppress the harmonics of the system to improve the current quality of the inverter.

[0142] In view of this design idea, the output current outer loop and the filter capacitor current inner loop both adopt the quasi-PR current control strategy. It can effectively track the given current, avoid the poor anti-interference performance and tracking performance of high-frequency signals of the traditional PI controller, and improve the stability of the system. The transfer function of the quasi-PR controller can be expressed as:

[0143]

[0144] In the formula, K p is the proportional gain, k nr is the resonant gain, ω nc is the cutoff frequency, ω n is the natural angular frequency, and s is a complex variable.

[0145] Through the control box Figure 4 It can be seen that the current error signal of the current outer loop is summed through proportional control and R control of each harmonic, and each separated harmonic is individually R controlled. The harmonic controller only performs high-gain steady-state control on integer fundamental frequencies, and the amplitude and phase margin of other frequency signals are low, thus suppressing the adverse effects of other frequencies and improving the control accuracy of each harmonic. The expressions of the fundamental control branch and the harmonic control branch are: Among them, K p is the proportional gain, k nr is the resonant gain, ω nc is the cutoff frequency, ω n is the natural angular frequency, and s is a complex variable.

[0146] This design can adaptively limit the current of harmonics by controlling the control switch K after the harmonic component extraction module. i To determine the compensation of each harmonic, in normal state or when the error is small, the switches of each harmonic branch are all closed, and full compensation of each harmonic can be achieved. Set a limit value. If the current error signal is large, due to the error exceeding the limit, the 5th, 7th, 11th, and 13th harmonics in the output current of the DG inverter are compensated first. At this time, only K5, K7, and K 11 , K 13 Wait for the control switch to be closed.

[0147] In addition, two working modes are designed for the grid current outer loop, corresponding to the input current I hThe two different values ​​of the selector switch position can be flexibly selected according to the inverter output capacity, so as to flexibly suppress and compensate the harmonics of the system. Mode 1: When the inverter carries less load, the selector switch is switched to mode 1, so that the system input current I h is the load current I L In this mode, the harmonic compensation of the load current can be realized directly without the harmonic detection module, which simplifies the control structure. Mode 2: When the inverter carries a large load, the switch is switched to mode 2, so that the input current I h is 0. At this time, the harmonic current limiting is adaptively performed according to the value of the current error signal, and the harmonics of the system are flexibly controlled and suppressed, thereby improving the current quality of the inverter.

[0148] This design uses a linear neural network algorithm to extract the harmonic components of the system. The harmonics in the system can be expressed as:

[0149]

[0150] Where t represents the time variable, b is the fundamental component, w is the angular frequency, w = 2πf, f is the frequency of the signal, ε is the harmonic order, A ε , ε are the amplitude and phase of the ε-th harmonic respectively.

[0151] Figure 7 The model diagram of harmonic extraction based on neural network is given. In the neural network model, the input matrix can be expressed as:

[0152] C=[c1(t),c2(t),c3(t),L,c 2n (t)]=[sin(wt),cos(wt),sin(2wt),cos(2wt),L,sin(nwt),cos(nwt)],

[0153] The weight matrix can be expressed as:

[0154] W=[w1(t),w2(t),w3(t),L,w 2n (t)]=[A1sinψ1,A1cosψ1,A2 sinψ2,A2cosψ2,L,A n sinψ n ,A n cosψ n ]

[0155] Therefore, the output vector of the neural network is the product of the input matrix and the weight matrix:

[0156]

[0157] The error can be obtained from the neural network model y(t) is the theoretical calibration value. After the error e(t) is processed by the LMS algorithm, the weight matrix W is readjusted according to the principle of minimizing the error target G. The error target function can be expressed as:

[0158]

[0159] Therefore, the harmonics are extracted through the neural network model, and the amplitude and phase of each harmonic can be expressed as:

[0160]

[0161] The above is an introduction to the method embodiment. The following is a further explanation of the disclosed solution through an apparatus embodiment.

[0162] Figure 8 A block diagram of a power grid control device based on a neural network and a current synchronization UI droop coefficient according to an embodiment of the present disclosure is shown.

[0163] like Figure 8 As shown, the device comprises:

[0164] The synchronous timing phasor measurement unit 801 is used to synchronously measure the voltage and current data of each node of the distributed power supply units in multiple substations;

[0165] The current uses radial basis function RBF neural network for current synchronization control, and the expression of current control is:

[0166]

[0167] Among them, k p and k i are the proportional and integral parameters of current synchronous control respectively. In RBF neural network control, x=[x c ] T is the network input, x c is the input of the cth neuron, [x c ] T is the transposed matrix of the input of the cth neuron, y = [y c ] T is the output of the hidden layer of the network, y c is the output of the cth neuron in the hidden layer, [y c ] T is the transposed matrix of the output of the cth neuron in the hidden layer;

[0168] The weights of the RBF neural network are w=[w1,w2,w3,...,w m ] T, m is the number of inputs, T is the calculation of the transposed matrix, and the output of the RBF neural network is:

[0169] y t =w T x=w1x1+w2x2+w3x3+...+w m x m ,

[0170] Through the RBF neural network algorithm, the q-axis current i q To a given value i qref Approximation is performed until the error is eliminated;

[0171] The current synchronization control module 802 is used to control the current synchronization when the system operation state suddenly changes and i qref -i q ≠0, the current is controlled by RBF neural network, and θ is adjusted continuously based on the current control expression. ref The size of q =0, the system reaches a stable state after adjustment;

[0172] The inductor-capacitor-inductor LCL filter 803 is used to perform high-frequency filtering on the distributed power supply unit in the station area in a stable state, and then connect it to the power grid or load. The control strategy of harmonic compensation adopts a current double closed-loop structure of an output current or grid current outer loop and a filter capacitor current inner loop; both the output current outer loop and the filter capacitor current inner loop adopt a quasi-proportional-resonant PR current control strategy;

[0173] Among them, the grid-connected current outer loop is composed of a proportional control branch, a fundamental wave control branch, and a harmonic control branch in parallel, wherein the harmonic suppression branch is used to suppress each harmonic component. When the current error is greater than a threshold, the 5th, 7th, 11th, and 13th harmonics with greater harmonics are preferentially suppressed.

[0174] In some embodiments, when I i >(I i +I j ) / 2, indicating that the equivalent impedance Z i <Z j , the current deviation is positive, and a droop parameter adjustment value greater than 0 is obtained through the PI regulator, which increases the droop parameter of the converter and improves the equivalent impedance Z of the converter. i , and finally achieve Z i =Z j , to achieve the current sharing effect; when I i <(I i +I j ) / 2, indicating that the equivalent impedance Z i >Zj , the current deviation is negative, and a droop parameter adjustment value less than 0 is obtained through the PI regulator, which reduces the droop parameter of the converter and reduces the equivalent impedance Z of the converter. i , and finally achieve Z i =Z j ; The droop coefficient after adaptive adjustment is γ i * =γ i +σ i As long as there is a current deviation ΔI, the droop factor will be adjusted continuously until ΔI is zero, where I i is the current value associated with node i, I j is the current value associated with node j, γ i is the original droop coefficient of node i, σ i is the corresponding correction coefficient of node i.

[0175] In some embodiments, the above-mentioned grid-connected current outer loop corresponds to two working modes. When the input current I h is the load current I L When the input current I h When it is 0, the harmonics of the system are controlled and suppressed.

[0176] In some embodiments, both the output current outer loop and the filter capacitor current inner loop adopt a quasi-PR current control strategy, and the transfer function of the quasi-PR controller is expressed as:

[0177]

[0178] Among them, K p is the proportional gain, k nr is the resonant gain, ω nc is the cutoff frequency, ω n is the natural angular frequency, and s is a complex variable.

[0179] In some embodiments, the current error signal of the current outer loop is summed through proportional control and proportional R control of each harmonic, and each separated harmonic is subjected to separate R control, and the harmonic controller only performs gain steady-state control on the integer fundamental frequency;

[0180] Among them, the expressions of the fundamental wave control branch and the harmonic wave control branch are:

[0181]

[0182] Among them, K p is the proportional gain, k nr is the resonant gain, ω nc is the cutoff frequency, ωn is the natural angular frequency, and s is a complex variable.

[0183] In some embodiments, a neural network algorithm may also be used to extract the harmonic components of the system, wherein the harmonics in the system are expressed as:

[0184]

[0185] Where t represents the time variable, b is the fundamental component, w is the angular frequency, w = 2πf, f is the frequency of the signal, ε is the harmonic order, A ε , ε are the amplitude and phase of the ε-th harmonic respectively.

[0186] In some embodiments, in the neural network model, the input matrix is ​​represented as:

[0187] C=[c1(t),c2(t),c3(t),L,c 2n (t)]=[sin(wt),cos(wt),sin(2wt),cos(2wt),L,sin(nwt),cos(nwt)],

[0188] The weight matrix is ​​expressed as:

[0189] W=[w1(t),w2(t),w3(t),L,w 2n (t)]=[A1 sinψ1,A1cosψ1,A2 sinψ2,A2 cosψ2,L,A n sinψ n ,A n cosψ n ],

[0190] The output vector of the neural network is the product of the input matrix and the weight matrix Getting the error from the neural network model y(t) is the theoretical calibration value. After the error e(t) is processed by the least mean square LMS algorithm, the weight matrix W is readjusted according to the principle of minimizing the error target G. The error target function is expressed as:

[0191]

[0192] Each harmonic is extracted through the neural network model, and the amplitude and phase of each harmonic are expressed as:

[0193]

[0194] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0195] Fig. 9 A block diagram of an exemplary electronic device capable of implementing an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0196] The device 900 includes a computing unit 901, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 902 or a computer program loaded from a storage unit 908 into a random access memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the device 900 can also be stored. The computing unit 901, the ROM 902, and the RAM 903 are connected to each other via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0197] A number of components in the device 900 are connected to the I / O interface 905, including: an input unit 906, such as a keyboard, a mouse, etc.; an output unit 907, such as various types of displays, speakers, etc.; a storage unit 908, such as a disk, an optical disk, etc.; and a communication unit 909, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 909 allows the device 900 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0198] The computing unit 901 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 901 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 901 performs the various methods and processes described above, such as Figure 1 For example, in some embodiments, Figure 1The method in the embodiment of the present invention may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as the storage unit 908. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 900 via the ROM 902 and / or the communication unit 909. When the computer program is loaded into the RAM 903 and executed by the computing unit 901, the above-described Figure 1 One or more steps of a method.

[0199] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0200] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0201] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0202] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0203] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0204] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0205] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of this disclosure can be achieved, and this document is not limited here.

[0206] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A power grid control method based on neural network and current synchronization UI droop coefficient, characterized in that: The method comprises: Use the synchronous timing phasor measurement unit to synchronously measure the voltage and current data of each node of the distributed power supply units in multiple substations; The current uses radial basis function RBF neural network for current synchronization control, and the expression of current control is: Among them, k p and k i are the proportional and integral parameters of current synchronous control respectively. In RBF neural network control, x=[x c ] T is the network input, x c is the input of the cth neuron, [x c ] T is the transposed matrix of the input of the cth neuron, y = [y c ] T is the output of the hidden layer of the network, y c is the output of the cth neuron in the hidden layer, [y c ] T is the transposed matrix of the output of the cth neuron in the hidden layer; The weights of the RBF neural network are w=[w1,w2,w3,...,w m ] T , m is the number of inputs, T is the calculation of the transposed matrix, and the output of the RBF neural network is: y t =w T x=w1x1+w2x2+w3x3+...+w m x m , Through the RBF neural network algorithm, the q-axis current i q To a given value i qref Approximation is performed until the error is eliminated; When the system operation state suddenly changes, i qref -i q ≠0, the current synchronization control module participates in the regulation, and the current adopts RBF neural network for current synchronization control, and continuously adjusts θ based on the expression of current control. ref The size of q =0, the system reaches a stable state after adjustment; An inductor-capacitor-inductor LCL filter is used to filter the distributed power supply units in the stable state at high frequency, and then connected to the power grid or load. The control strategy for harmonic compensation adopts a current double closed-loop structure of the output current or grid current outer loop and the filter capacitor current inner loop; both the output current outer loop and the filter capacitor current inner loop adopt a quasi-proportional-resonant PR current control strategy; The grid-connected current outer loop is composed of a proportional control branch, a fundamental wave control branch, and a harmonic control branch in parallel, wherein the harmonic suppression branch is used to suppress each harmonic component. When the current error is greater than a threshold, the 5th, 7th, 11th, and 13th harmonics with greater harmonics are preferentially suppressed.

2. The method according to claim 1, characterized in that The method further comprises: When I i >(I i +I j ) / 2, indicating that the equivalent impedance Z i <Z j , the current deviation is positive, and a droop parameter adjustment value greater than 0 is obtained through the PI regulator, which increases the droop parameter of the converter and improves the equivalent impedance Z of the converter. i , and finally achieve Z i =Z j , to achieve the current sharing effect; when I i <(I i +I j ) / 2, indicating that the equivalent impedance Z i >Z j , the current deviation is negative, and a droop parameter adjustment value less than 0 is obtained through the PI regulator, which reduces the droop parameter of the converter and reduces the equivalent impedance Z of the converter. i , and finally achieve Z i =Z j ; The droop coefficient after adaptive adjustment is γ i * =γ i +σ i As long as there is a current deviation ΔI, the droop factor will be adjusted continuously until ΔI is zero, where I i is the current value associated with node i, I j is the current value associated with node j, γ i is the original droop coefficient of node i, σ i is the corresponding correction coefficient of node i.

3. The method according to claim 1, characterized in that The grid current outer loop corresponds to two working modes. h is the load current I L When the input current I h When it is 0, the harmonics of the system are controlled and suppressed.

4. The method according to claim 1, characterized in that: The output current outer loop and the filter capacitor current inner loop both adopt the quasi-PR current control strategy. The transfer function of the quasi-PR controller is expressed as: Among them, K p is the proportional gain, k nr is the resonant gain, ω nc is the cutoff frequency, ω n is the natural angular frequency, and s is a complex variable.

5. The method according to claim 1, characterized in that The current error signal of the current outer loop is summed through proportional control and proportional R control of each harmonic, and each separated harmonic is subjected to separate R control. The harmonic controller only performs gain steady-state control on the integer fundamental frequency. Among them, the expressions of the fundamental wave control branch and the harmonic wave control branch are: Among them, K p is the proportional gain, k nr is the resonant gain, ω nc is the cutoff frequency, ω n is the natural angular frequency, and s is a complex variable.

6. The method according to claim 1, characterized in that The method further comprises: The neural network algorithm is used to extract the harmonic components of the system, where the harmonics in the system are expressed as: Where t represents the time variable, b is the fundamental component, w is the angular frequency, w = 2πf, f is the frequency of the signal, ε is the harmonic order, A ε , ε are the amplitude and phase of the ε-th harmonic respectively.

7. The method according to claim 6, characterized in that In the neural network model, the input matrix is ​​represented as: C[c1(t),c2(t),c3(t),L,c 2n (t)]s[sin(wt),cos(wt),sin(2wt),cos(2wt),L,sin(nwt),cos(nwt)] The weight matrix is ​​expressed as: W=[w1(t),w2(t),w3(t),L,w 2n (t)]=[A1 sinψ1,A1cosψ1,A2 sinψ2,A2 cosψ2,L,A n sinψ n ,TO n cosψ n ], The output vector of the neural network is the product of the input matrix and the weight matrix Getting the Error from the Neural Network Model y(t) is the theoretical calibration value. After the error e(t) is processed by the least mean square LMS algorithm, the weight matrix W is readjusted according to the principle of minimizing the error target G. The error target function is expressed as: Each harmonic is extracted through the neural network model, and the amplitude and phase of each harmonic are expressed as:

8. A power grid control device based on neural network and current synchronization UI droop coefficient, used to implement the method described in any one of claims 1 to 7, characterized in that: The device comprises: The synchronous timing phasor measurement unit is used to synchronously measure the voltage and current data of each node of the distributed power supply units in multiple substations; The current uses radial basis function RBF neural network for current synchronization control, and the expression of current control is: Among them, k p and k i are the proportional and integral parameters of current synchronous control respectively. In RBF neural network control, x=[x c ] T is the network input, x c is the input of the cth neuron, [x c ] T is the transposed matrix of the input of the cth neuron, y = [y c ] T is the output of the hidden layer of the network, y c is the output of the cth neuron in the hidden layer, [y c ] T is the transposed matrix of the output of the cth neuron in the hidden layer; The weights of the RBF neural network are w=[w1,w2,w3,...,w m ] T , m is the number of inputs, T is the calculation of the transposed matrix, and the output of the RBF neural network is: y t =w T x=w1x1+w2x2+w3x3+...+w m x m , Through the RBF neural network algorithm, the q-axis current i q To a given value i qref Approximation is performed until the error is eliminated; The current synchronization control module is used when the system operating state suddenly changes and i qref -i q ≠0, the current is controlled by RBF neural network, and θ is adjusted continuously based on the current control expression. ref The size of q =0, the system reaches a stable state after adjustment; The inductor-capacitor-inductor LCL filter is used to filter the distributed power supply units in the stable state at high frequencies, and then connect them to the grid or load. The control strategy for harmonic compensation adopts a current double closed-loop structure of the output current or grid current outer loop and the filter capacitor current inner loop; both the output current outer loop and the filter capacitor current inner loop adopt a quasi-proportional-resonant PR current control strategy; Among them, the grid-connected current outer loop is composed of a proportional control branch, a fundamental wave control branch, and a harmonic control branch in parallel, wherein the harmonic suppression branch is used to suppress each harmonic component. When the current error is greater than a threshold, the 5th, 7th, 11th, and 13th harmonics with greater harmonics are preferentially suppressed.

9. An electronic device, characterized in that: include: at least one processor; and a memory communicatively coupled to the at least one processor; It is characterized in that the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-7.

Images